Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling

2008 ◽  
Vol 12 (4) ◽  
pp. 447-458 ◽  
Author(s):  
M.G. Tsipouras ◽  
T.P. Exarchos ◽  
D.I. Fotiadis ◽  
A.P. Kotsia ◽  
K.V. Vakalis ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Fuzzy Modeling ◽  
Automated Diagnosis ◽  
Artery Disease

A data mining approach for diagnosis of coronary artery disease

2013 ◽  
Vol 111 (1) ◽  
pp. 52-61 ◽  
Author(s):  
Roohallah Alizadehsani ◽  
Jafar Habibi ◽  
Mohammad Javad Hosseini ◽  
Hoda Mashayekhi ◽  
Reihane Boghrati ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Data Mining Approach ◽  
Artery Disease

An Improved C4.5 Data Mining Driven Algorithm for the Diagnosis of Coronary Artery Disease

2019 ◽  
Author(s):  
Ahmed Abba Haruna ◽  
L. J. Muhammad ◽  
B. Z. Yahaya ◽  
E. J. Garba ◽  
N. D. Oye ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Artery Disease

Automated Diagnostics of Coronary Artery Disease

2012 ◽  
pp. 91-112
Author(s):  
Matjaž Kukar ◽  
Igor Kononenko ◽  
Ciril Grošelj
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Coronary Artery Disease ◽  
Feature Extraction ◽  
Coronary Artery ◽  
Diagnostic Process ◽  
Long Term Study ◽  
Post Test ◽  
Artery Disease

The authors present results and the latest advancement in their long-term study on using image processing and data mining methods in medical image analysis in general, and in clinical diagnostics of coronary artery disease in particular. Since the evaluation of modern medical images is often difficult and time-consuming, authors integrate advanced analytical and decision support tools in diagnostic process. Partial diagnostic results, frequently obtained from tests with substantial imperfections, can be thus integrated in ultimate diagnostic conclusion about the probability of disease for a given patient. Authors study various topics, such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform the medical practice. During their long-term study (1995-2011) authors achieved, among other minor results, two really significant milestones. The first was achieved by using machine learning to significantly increase post-test diagnostic probabilities with respect to expert physicians. The second, even more significant result utilizes various advanced data analysis techniques, such as automatic multi-resolution image parameterization combined with feature extraction and machine learning methods to significantly improve on all aspects of diagnostic performance. With the proposed approach clinical results are significantly as well as fully automatically, improved throughout the study. Overall, the most significant result of the work is an improvement in the diagnostic power of the whole diagnostic process. The approach supports, but does not replace, physicians’ diagnostic process, and can assist in decisions on the cost-effectiveness of diagnostic tests.

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